To address the challenges like:
researchers have developed a computational approach based on standard hematoxylin and eosin–stained tissue sections and demonstrated its power in a discovery and validation cohort of 323 and 241 breast tumors, respectively.
The results were published in Sci. Transl. Med. 4, 157ra143 (2012).
To deconvolute cellular heterogeneity and detect subtle genomic aberrations, they introduced an algorithm based on tumor cellularity to increase the comparability of copy number profiles between samples and then devised a predictor for survival in estrogen receptor–negative breast cancer that integrated both image-based and gene expression analyses. It appears that it significantly outperformed classifiers that use single data types, such as microarray expression signatures.
Image processing also allowed them to describe and validate an independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays.
Authors conclude that quantitative, image-based method could benefit any large-scale cancer study by refining and complementing molecular assays of tumor samples.
Citation: Y. Yuan, H. Failmezger, O. M. Rueda, H. R. Ali, S. Gräf, S.-F. Chin, R. F. Schwarz, C. Curtis, M. J. Dunning, H. Bardwell, N. Johnson, S. Doyle, G. Turashvili, E. Provenzano, S. Aparicio, C. Caldas, F. Markowetz, Quantitative Image Analysis of Cellular Heterogeneity in Breast Tumors Complements Genomic Profiling. Sci. Transl. Med. 4, 157ra143 (2012).
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